The traditional counterfactual learning approaches usually re-weight samples based on propensity scores [1]}[2]}[3]}[4]}[5]}[6]}.
Propensity scores indicate the probabilities of observation under different environments, e.g., approval and rejection in the credit scoring scheme.
These propensity score-based methods and re-weighting approaches in reject inference [7]}[8]}[9]} are similar, and both try to balance the data distributions of observed and unobserved samples.
Stable learning is another perspective of counterfactual learning, in which there is no implicit treatments and the distribution of unobserved samples is unknown [10]}.
Stable learning is usually done via decorrelation among features of samples, which tries to make the feature distribution closer to independently identically distribution [11]}[12]}[13]}.
Meanwhile, Sample Reweighted Decorrelation Operator (SRDO) [12]} generates some unobserved samples, and trains a binary classifier to get the probabilities of observation for re-weighting the observed samples.
This is somehow similar to the propensity score-based approaches.
